Development Roadmap

Authors: Joe Hamman and Ryan Abernathey Date: February 7, 2019

Background and scope

Xbatcher is a small library for iterating xarray objects in batches. The goal is to make it easy to feed xarray datasets to machine learning libraries such as Keras or PyTorch. For example, implementing a simple machine learning workflow may look something like this:

import xarray as xr
import xbatcher as xb

da = xr.open_dataset(filename, chunks=chunks)     # open a dataset and use dask
da_train = preprocess(ds)                         # perform some preprocessing
bgen = xb.BatchGenerator(da_train, {'time': 10})  # create a generator

for batch in bgen:                                # iterate through the generator['x'], batch['y'])             # fit a deep-learning model
    # or
    model.predict(batch['x'])                     # make one batch of predictions

We are currently envisioning the project growing to support more complex extract-transform-load components commonly found in machine learning workflows that use multidimensional data. We note that many of the concepts in Xbatcher have been developed through collaborations in the Pangeo Project Machine Learning Working Group.

Batch generation

At the core of Xbatcher is the ability to define a schema that defines a selection of a larger dataset. Today, this schema is fairly simple (e.g. {‘time’: 10}) but this may evolve in the future. As we describe below, additional utilities for shuffling, sampling, and caching may provide enhanced batch generation functionality

Shuffle and Sampling APIs

When training machine-learning models in batches, it is often necessary to selectively or randomly sample from your training data. Xbatcher can help facilitate seamless shuffling and sampling by providing APIs that operate on batches and/or full datasets. This may require working with Xarray and Dask to facilitate fast, distributed shuffles of Dask arrays.

Caching APIs

A common pattern in ML is perform the ETL tasks once before saving the results to a local file system. This is an effective approach for speeding up dataset loading during training but comes with numerous downsides (i.e. requires sufficient file space, breaks workflow continuity, etc.). We propose the development of a pluggable cache mechanism in Xbatcher that would help address these downsides while providing improved performance during model training and inference. For example, this pluggable cache mechanism may allow choosing between multiple cache types, such as an LRU in-memory cache, a Zarr filesystem or S3 bucket, or a Redis database cache.

Integration with TensorFlow and PyTorch Dataset Loaders

Deep-learning libraries like TensorFlow and PyTorch provide high-performance dataset-generator APIs that facilitate the construction of flexible and efficient input pipelines. In particular, they have been optimized to support asynchronous data loading and training, transfer to and from GPUs, and batch caching. Xbatcher will provide compatible dataset APIs that allow users to pass Xarray datasets directly to deep-learning frameworks.


  • Core: Xarray, Pandas, Dask, Scikit-learn, Numpy, Scipy

  • Optional: Keras, PyTorch, Tensorflow, etc.